Summarizing Services of Java Packages

Abstract

Abstract-Program comprehension is essential for code maintenance and evolution activities. It saves time and efforts of developers who want to perform any code changes. It also minimizes the chances of introducing bugs. Textual summaries for source code provide great help to code understanding activities. This paper presents an approach to automatically generate textual summaries for services implemented in java packages. The summary is generated by analyzing the source code of methods defined the package. Each method represents a service provide by the package. Each service is summarized as a natural language textual description. The generated summary for a method mainly includes the used data and the names of invoked methods. Summaries of all methods defined in a package are refined and integrated to be reported as a comprehensive summary for the services provided by the package. The generated summaries are useful in different ways. They can be used by developers in their maintenance activities. They also can be useful for the documentation purposes. IndexTerms-Program comprehension, software maintenance, source code summarization.

The Source Code Comment Generation based on Abstract Syntax Tree

“…Trace its technological development, at first, the dominant strategy was based on sentence templates and heuristics derived from empirical studies [6][7][8][9][10]. Starting around 2016, data-driven strategies based on neural networks came to the forefront, leveraging gains from both the AI/NLP and mining software repositories research communities [11][12][13][14].…”

The Source Code Comment Generation Based on Deep Reinforcement Learning and Hierarchical Attention

“…Trace its technological development,at first, the dominant strategy was based on sentence templates and heuristics derived from empirical studies [6][7][8][9][10]. Starting around 2016, data-driven strategies based on neural networks came to the forefront, leveraging gains from both the AI/NLP and mining software repositories research communities [11][12][13][14].As far as we know, the existing deep learning based comment generation approaches mainly utilize the seq2seq model in which the program code is encoded into hidden space first and then decode it to produce the target comment.However, these kind of approaches have the following drawbacks: (1) they mainly take the source code as plain text and ignore the hierarchical structure of the source code; (2) most of the approaches only consider simple features,such as,tokens,which overlooking the hidden information that can help grab the relationships between source code and comments; (3) they typically train the decoder to produce the code annotation by calculating and maximizing the odds based on the subsequent natural language words, however in fact, they mainly produce the code annotation from scratch.…”